Merging toward natural clusters

Zhigang Tan; Nelson Hon Ching Yung

doi:10.1117/1.3183892

1 July 2009 Merging toward natural clusters

Zhigang Tan, Nelson Hon Ching Yung

Author Affiliations +

Optical Engineering, Vol. 48, Issue 7, 077202 (July 2009). https://doi.org/10.1117/1.3183892

Abstract

To findout how many clusters exist in a sample set is an old yet unsolved problem in unsupervised clustering. This problem inevitably occurs in region merging/growing, a well studied and popular technique in image segmentation. Region merging usually needs a stop criterion. The stop criterion is not automatically determined and often has to be set manually to arrive at a sensible segmentation, which is rather difficult for natural images. To address this problem, we present a robust stop criterion that is based on a novel distinctness predicate for adjacent regions. The predicate discerns distinct regions by examining the evidence of the boundary between neighboring regions. Requiring that every region should be distinct from each other, the proposed method is able to choose a stop point where a natural partition is most likely. Under a region merging framework, we demonstrate the effectiveness of the stop criterion using two merging criterion: one based on optimizing a global functional, and another based on a local criterion. Experimental results and comparison are given at the end.

©(2009) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Zhigang Tan and Nelson Hon Ching Yung "Merging toward natural clusters," Optical Engineering 48(7), 077202 (1 July 2009). https://doi.org/10.1117/1.3183892

Published: 1 July 2009

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available